草地生态系统土壤呼吸及其影响因素研究进展

土壤呼吸在全球碳收支中占有重要的地位,笔者对草地生态系统土壤呼吸在陆地生态系统碳平衡中的作用、土壤呼吸的分类及其影响因素等方面进行了综述。结果表明,草地生态系统土壤呼吸在不同时间空间各组分所占比例不同,生物、非生物及人为活动等因素对草地土壤呼吸影响各异,主要从土壤温度、气候变暖、土壤湿度、降水、干旱化、土壤C/N等非生物因素,叶面积指数、植物光合作用、植被凋落物等生物因素以及人类干扰活动等方面具体阐述这些因素变化对土壤呼吸产生的影响,并对草地土壤呼吸的Q10值及各影响因素间的交互作用进行归纳总结。提出草地生态系统土壤呼吸研究存在的问题和今后重点发展方向,并对未来草地生态系统土壤呼吸的研究工作做了进一步的展望。

Application of Dempster-Shafer evidence theory to unsupervised classification in multisource remote sensing

The aim of this paper is to show that Dempster-Shafer evidence theory may be successfully applied to unsupervised classification in multisource remote sensing. Dempster-Shafer formulation allows for consideration of unions of classes, and to represent both imprecision and uncertainty, through the definition of belief and plausibility functions. These two functions, derived from mass function, are generally chosen in a supervised way. In this paper, the authors describe an unsupervised method, based on the comparison of monosource classification results, to select the classes necessary for Dempster-Shafer evidence combination and to define their mass functions. Data fusion is then performed, discarding invalid clusters (e.g. corresponding to conflicting information) thank to an iterative process. Unsupervised multisource classification algorithm is applied to MAC-Europe'91 multisensor airborne campaign data collected over the Orgeval French site. Classification results using different combinations of sensors (TMS and AirSAR) or wavelengths (L- and C-bands) are compared. Performance of data fusion is evaluated in terms of identification of land cover types. The best results are obtained when all three data sets are used. Furthermore, some other combinations of data are tried, and their ability to discriminate between the different land cover types is quantified

Decision tree classification of land cover from remotely sensed data

Decision tree classification algorithms have significant potential for land cover mapping problems and have not been tested in detail by the remote sensing community relative to more conventional pattern recognition techniques such as maximum likelihood classification. In this paper, we present several types of decision tree classification algorithms arid evaluate them on three different remote sensing data sets. The decision tree classification algorithms tested include an univariate decision tree, a multivariate decision tree, and a hybrid decision tree capable of including several different types of classification algorithms within a single decision tree structure. Classification accuracies produced by each of these decision tree algorithms are compared with both maximum likelihood and linear discriminant function classifiers. Results from this analysis show that the decision tree algorithms consistently outperform the maximum likelihood and linear discriminant function classifiers in regard to classf — cation accuracy. In particular, the hybrid tree consistently produced the highest classification accuracies for the data sets tested. More generally, the results from this work show that decision trees have several advantages for remote sensing applications by virtue of their relatively simple, explicit, and intuitive classification structure. Further, decision tree algorithms are strictly nonparametric and, therefore, make no assumptions regarding the distribution of input data, and are flexible and robust with respect to nonlinear and noisy relations among input features and class labels.

Predicting gene function in Saccharomyces cerevisiae

Clare, A. and King R.D. (2003) Predicting gene function in Saccharomyces cerevisiae. 2nd European Conference on Computational Biology (ECCB '03). (published as a journal supplement in Bioinformatics 19: ii42-ii49)

Hierarchical Multi-classification with Predictive Clustering Trees in Functional Genomics

Struyf, J., Dzeroski, S. Blockeel, H. and Clare, A. (2005) Hierarchical Multi-classification with Predictive Clustering Trees in Functional Genomics. In proceedings of the EPIA 2005 CMB Workshop

Integration of relational and hierarchical network information for protein function prediction

BACKGROUND:In the current climate of high-throughput computational biology, the inference of a protein's function from related measurements, such as protein-protein interaction relations, has become a canonical task. Most existing technologies pursue this task as a classification problem, on a term-by-term basis, for each term in a database, such as the Gene Ontology (GO) database, a popular rigorous vocabulary for biological functions. However, ontology structures are essentially hierarchies, with certain top to bottom annotation rules which protein function predictions should in principle follow. Currently, the most common approach to imposing these hierarchical constraints on network-based classifiers is through the use of transitive closure to predictions.RESULTS:We propose a probabilistic framework to integrate information in relational data, in the form of a protein-protein interaction network, and a hierarchically structured database of terms, in the form of the GO database, for the purpose of protein function prediction. At the heart of our framework is a factorization of local neighborhood information in the protein-protein interaction network across successive ancestral terms in the GO hierarchy. We introduce a classifier within this framework, with computationally efficient implementation, that produces GO-term predictions that naturally obey a hierarchical 'true-path' consistency from root to leaves, without the need for further post-processing.CONCLUSION:A cross-validation study, using data from the yeast Saccharomyces cerevisiae, shows our method offers substantial improvements over both standard 'guilt-by-association' (i.e., Nearest-Neighbor) and more refined Markov random field methods, whether in their original form or when post-processed to artificially impose 'true-path' consistency. Further analysis of the results indicates that these improvements are associated with increased predictive capabilities (i.e., increased positive predictive value), and that this increase is consistent uniformly with GO-term depth. Additional in silico validation on a collection of new annotations recently added to GO confirms the advantages suggested by the cross-validation study. Taken as a whole, our results show that a hierarchical approach to network-based protein function prediction, that exploits the ontological structure of protein annotation databases in a principled manner, can offer substantial advantages over the successive application of 'flat' network-based methods.

Automatic classification of field-collected dinoflagellates by artificial neural network

Automatic taxonomic categorisation of 23 species of dinoflagellates was demonstrated using field-collected specimens. These dinoflagellates have been responsible for the majority of toxic and noxious phytoplankton blooms which have occurred in the coastal waters of the European Union in recent years and make severe impact on the aquaculture industry. The performance by human 'expert' ecologists/taxonomists in identifying these species was compared to that achieved by 2 artificial neural network classifiers (multilayer perceptron and radial basis function networks) and 2 other statistical techniques, k-Nearest Neighbour and Quadratic Discriminant Analysis. The neural network classifiers outperform the classical statistical techniques. Over extended trials, the human experts averaged 85% while the radial basis network achieved a best performance of 83%, the multilayer perceptron 66%, k-Nearest Neighbour 60%, and the Quadratic Discriminant Analysis 56%.

Analysis of classification accuracy for pre-filtered multichannel remote sensing data

Noise is one of the main factors degrading the quality of original multichannel remote sensing data and its presence influences classification efficiency, object detection, etc. Thus, pre-filtering is often used to remove noise and improve the solving of final tasks of multichannel remote sensing. Recent studies indicate that a classical model of additive noise is not adequate enough for images formed by modern multichannel sensors operating in visible and infrared bands. However, this fact is often ignored by researchers designing noise removal methods and algorithms. Because of this, we focus on the classification of multichannel remote sensing images in the case of signal-dependent noise present in component images. Three approaches to filtering of multichannel images for the considered noise model are analysed, all based on discrete cosine transform in blocks. The study is carried out not only in terms of conventional efficiency metrics used in filtering (MSE) but also in terms of multichannel data classification accuracy (probability of correct classification, confusion matrix). The proposed classification system combines the pre-processing stage where a DCT-based filter processes the blocks of the multichannel remote sensing image and the classification stage. Two modern classifiers are employed, radial basis function neural network and support vector machines. Simulations are carried out for three-channel image of Landsat TM sensor. Different cases of learning are considered: using noise-free samples of the test multichannel image, the noisy multichannel image and the pre-filtered one. It is shown that the use of the pre-filtered image for training produces better classification in comparison to the case of learning for the noisy image. It is demonstrated that the best results for both groups of quantitative criteria are provided if a proposed 3D discrete cosine transform filter equipped by variance stabilizing transform is applied. The classification results obtained for data pre-filtered in different ways are in agreement for both considered classifiers. Comparison of classifier performance is carried out as well. The radial basis neural network classifier is less sensitive to noise in original images, but after pre-filtering the performance of both classifiers is approximately the same.

The Impact of Polystyrene Microplastics on Feeding, Function and Fecundity in the Marine CopepodCalanus helgolandicus

Microscopic plastic debris, termed “microplastics”, are of increasing environmental concern. Recent studies have demonstrated that a range of zooplankton, including copepods, can ingest microplastics. Copepods are a globally abundant class of zooplankton that form a key trophic link between primary producers and higher trophic marine organisms. Here we demonstrate that ingestion of microplastics can significantly alter the feeding capacity of the pelagic copepod Calanus helgolandicus. Exposed to 20 μm polystyrene beads (75 microplastics mL–1) and cultured algae ([250 μg C L–1) for 24 h, C. helgolandicus ingested 11% fewer algal cells (P = 0.33) and 40% less carbon biomass (P < 0.01). There was a net downward shift in the mean size of algal prey consumed (P < 0.001), with a 3.6 fold increase in ingestion rate for the smallest size class of algal prey (11.6–12.6 μm), suggestive of postcapture or postingestion rejection. Prolonged exposure to polystyrene microplastics significantly decreased reproductive output, but there were no significant differences in egg production rates, respiration or survival. We constructed a conceptual energetic (carbon) budget showing that microplastic-exposed copepods suffer energetic depletion over time. We conclude that microplastics impede feeding in copepods, which over time could lead to sustained reductions in ingested carbon biomass.

Psychophysical Function in Age-related Maculopathy

Age-related macular degeneration (AMD), the late stage of age-related maculopathy (ARM), is the leading cause of blind registration in developed Countries. The Visual loss in AMD occurs due to dysfunction and death of photoreceptors (rods and cones) secondary to an atrophic or a neovascular event. The psychophysical tests of vision, which depend on the functional status of the photoreceptors, may detect subtle alterations in the macula before morphological fundus changes are apparent ophthalmoscopically, and before traditional measures of visual acuity exhibit deterioration, and may be a useful tool for assessing and monitoring patients with ARM. Furthermore, worsening of these visual functions over time may reflect disease progression, and some of these, alone or iti combination with other parameters, may act as a prognostic indicator for identifying eyes at, risk for developing neovascular AMD. Lastly, psychophysical tests often correlate with subjective and relatively undefined symptoms in patients With early ARM, and may reflect limitation of daily activities for ARM patients. However, clinical studies investigating psychophysical function have largely been cross-sectional in nature, with small sample sizes, and lack consistency in terms Of the grading and classification of ARM. This article aims to comprehensively review the literature germane to psychophysical tests in ARM, and to furnish the reader with an insight into this complex area of research. (Surv Ophdialmol 54:167-210, 2009. (C) 2009 Elsevier Inc. All rights reserved.)

Visual function and dysfunction in early and late age-related maculopathy

Late age-related maculopathy (ARM) is responsible for the majority of blind registrations in the Western world among persons over 50 years of age. It has devastating effects on quality of life and independence and is becoming a major public health concern. Current treatment options are limited and most aim to slow progression rather than restore vision; therefore, early detection to identify those patients most suitable for these interventions is essential. In this work, we review the literature encompassing the investigation of visual function in ARM in order to highlight those visual function parameters which are affected very early in the disease process. We pay particular attention to measures of acuity, contrast sensitivity (CS), cone function, electrophysiology, visual adaptation, central visual field sensitivity and metamorphopsia. We also consider the impact of bilateral late ARM on visual function as well as the relationship between measures of vision function and self-reported visual functioning. Much interest has centred on the identification of functional changes which may predict progression to neovascular disease; therefore, we outline the longitudinal studies, which to date have reported dark-adaptation time, short-wavelength cone sensitivity, colour-match area effect, dark-adapted foveal sensitivity, foveal flicker sensitivity, slow recovery from glare and slower foveal electroretinogram implicit time as functional risk factors for the development of neovascular disease. Despite progress in this area, we emphasise the need for longitudinal studies designed in light of developments in disease classification and retinal imaging, which would ensure the correct classification of cases and controls, and provide increased understanding of the natural course and progression of the disease and further elucidate the structure-function relationships in this devastating disorder.

Patient classification for intelligent wheelchair adaptation

A importância e preocupação dedicadas à autonomia e independência das pessoas idosas e dos pacientes que sofrem de algum tipo de deficiência tem vindo a aumentar significativamente ao longo das últimas décadas. As cadeiras de rodas inteligentes (CRI) são tecnologias que podem ajudar este tipo de população a aumentar a sua autonomia, sendo atualmente uma área de investigação bastante ativa. Contudo, a adaptação das CRIs a pacientes específicos e a realização de experiências com utilizadores reais são assuntos de estudo ainda muito pouco aprofundados. A cadeira de rodas inteligente, desenvolvida no âmbito do Projeto IntellWheels, é controlada a alto nível utilizando uma interface multimodal flexível, recorrendo a comandos de voz, expressões faciais, movimentos de cabeça e através de joystick. Este trabalho teve como finalidade a adaptação automática da CRI atendendo às características dos potenciais utilizadores. Foi desenvolvida uma metodologia capaz de criar um modelo do utilizador. A investigação foi baseada num sistema de recolha de dados que permite obter e armazenar dados de voz, expressões faciais, movimentos de cabeça e do corpo dos pacientes. A utilização da CRI pode ser efetuada em diferentes situações em ambiente real e simulado e um jogo sério foi desenvolvido permitindo especificar um conjunto de tarefas a ser realizado pelos utilizadores. Os dados foram analisados recorrendo a métodos de extração de conhecimento, de modo a obter o modelo dos utilizadores. Usando os resultados obtidos pelo sistema de classificação, foi criada uma metodologia que permite selecionar a melhor interface e linguagem de comando da cadeira para cada utilizador. A avaliação para validação da abordagem foi realizada no âmbito do Projeto FCT/RIPD/ADA/109636/2009 - "IntellWheels - Intelligent Wheelchair with Flexible Multimodal Interface". As experiências envolveram um vasto conjunto de indivíduos que sofrem de diversos níveis de deficiência, em estreita colaboração com a Escola Superior de Tecnologia de Saúde do Porto e a Associação do Porto de Paralisia Cerebral. Os dados recolhidos através das experiências de navegação na CRI foram acompanhados por questionários preenchidos pelos utilizadores. Estes dados foram analisados estatisticamente, a fim de provar a eficácia e usabilidade na adequação da interface da CRI ao utilizador. Os resultados mostraram, em ambiente simulado, um valor de usabilidade do sistema de 67, baseado na opinião de uma amostra de pacientes que apresentam os graus IV e V (os mais severos) de Paralisia Cerebral. Foi também demonstrado estatisticamente que a interface atribuída automaticamente pela ferramenta tem uma avaliação superior à sugerida pelos técnicos de Terapia Ocupacional, mostrando a possibilidade de atribuir automaticamente uma linguagem de comando adaptada a cada utilizador. Experiências realizadas com distintos modos de controlo revelaram a preferência dos utilizadores por um controlo compartilhado com um nível de ajuda associado ao nível de constrangimento do paciente. Em conclusão, este trabalho demonstra que é possível adaptar automaticamente uma CRI ao utilizador com claros benefícios a nível de usabilidade e segurança.

Structure-function relationships in a glycosyltransferase, a phosphatase and an oxidoreductase

Dissertation presented to obtain the Ph.D degree in Biochemistry

AutoFACT: An Automatic Functional Annotation and Classification Tool

Affiliation: Centre Robert-Cedergren de l'Université de Montréal en bio-informatique et génomique & Département de biochimie, Université de Montréal

Non invasive classification system of scoliosis curve types using least-squares support vector machines

Objective To determine scoliosis curve types using non invasive surface acquisition, without prior knowledge from X-ray data. Methods Classification of scoliosis deformities according to curve type is used in the clinical management of scoliotic patients. In this work, we propose a robust system that can determine the scoliosis curve type from non invasive acquisition of the 3D back surface of the patients. The 3D image of the surface of the trunk is divided into patches and local geometric descriptors characterizing the back surface are computed from each patch and constitute the features. We reduce the dimensionality by using principal component analysis and retain 53 components using an overlap criterion combined with the total variance in the observed variables. In this work, a multi-class classifier is built with least-squares support vector machines (LS-SVM). The original LS-SVM formulation was modified by weighting the positive and negative samples differently and a new kernel was designed in order to achieve a robust classifier. The proposed system is validated using data from 165 patients with different scoliosis curve types. The results of our non invasive classification were compared with those obtained by an expert using X-ray images. Results The average rate of successful classification was computed using a leave-one-out cross-validation procedure. The overall accuracy of the system was 95%. As for the correct classification rates per class, we obtained 96%, 84% and 97% for the thoracic, double major and lumbar/thoracolumbar curve types, respectively. Conclusion This study shows that it is possible to find a relationship between the internal deformity and the back surface deformity in scoliosis with machine learning methods. The proposed system uses non invasive surface acquisition, which is safe for the patient as it involves no radiation. Also, the design of a specific kernel improved classification performance.